从电话后续访谈中判断临床结果的大型语言模型:多中心随机临床试验的二次分析
Zhao Shi1, Bingqian Wu2, Bin Hu1
1Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Nature communications
|December 1, 2025
概括
一个新的特定领域的大型语言模型 (Fu-LLM) 自动化了从电话采访中判断临床结果. 在临床试验中,Fu-LLM超越了一般的LLM和人类专家,提高了临床试验的效率和准确性.
科学领域:
- 临床试验方法论 临床试验方法论
- 医疗保健中的人工智能
- 医疗信息学医学信息学
背景情况:
- 在大规模试验中,从电话随访中自动判断临床结果至关重要,以减少工作量和提高数据质量.
- 目前的方法通常涉及手动审查,这耗时且容易产生不一致.
研究的目的:
- 开发和评估一个特定于领域的大型语言模型 (Fu-LLM),用于自动化从电话后续采访中预先判断临床事件.
- 为了比较Fu-LLM与通用LLM和人类评判员的表现.
主要方法:
- 一个特定领域的大型语言模型 (Fu-LLM) 在中国CT-FFR研究3的1,046个后续电话访谈小组上进行了训练和测试.
- 通过比较Fu-LLM对临床事件 (死亡,住院,药物使用) 的自动预判,与最先进的通用LLM,传统机器学习模型和人类判断器进行性能评估.
- 进行了时间漂移测试,以评估Fu-LLM与GPT-4的不同版本相比的强度.
主要成果:
- 与通用LLM (GPT-3.5-turbo,GPT-4o,DeepSeek-v3,Claude 3.5-Sonnet,Gemini-2.0-Pro) 和支持矢量机器相比,Fu-LLM在自动化关键临床事件预判方面表现出更好的表现.
- 在一项in silico比较研究中,Fu-LLM的表现优于人类评委.
- 该模型在抗时间漂移方面表现出比各种GPT-4版本更强大的稳定性.
结论:
- 在大规模的随机临床试验中,Fu-LLM提供了一个可扩展和准确的解决方案,用于自动化劳动密集型的临床结果识别过程.
- 这项技术可以显著简化试验操作,提高数据质量,降低总体成本.
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